In previous patent application 60/685,464, by the same inventor, a neural network was disclosed that learns to associate VA interval with temporal patterns of a hemodynamic sensor. The associated VA interval was used to replace the natural sensed atrial events during atrial fibrillation episodes. In addition, the patent suggested another preferred embodiment wherein the associated VA interval would replace the sensed atrial event during normal sinus rhythm as well as long as the associated signal is valid.
The associated VA interval with a temporal pattern of a hemodynamic sensor, cited above, is a prediction method based on learning to associate an input signal with another preceding input signal pattern. The associative learning paradigm uses timing causality instead of a physical model for solving the underlying system dynamics. The learning paradigm associates a solution, for example a VA interval with a temporal pattern of a hemodynamic sensor that reflects the physical system behaviour without the need to know and describe the detailed initial state, forces and interactions that determine the cardiac muscle behaviour. Hence, it may be used as an alternative paradigm to approaches like Newtonian equations of motion in classical mechanics or finite elements calculations of the electromechanical behaviour of the heart that determines the system dynamics by modelling the underlying physical forces and interactions. The advantage of the associative learning paradigm according to the system sensed parameters is that with a complex system, that might have unknown internal structure and internal states, associative learning can produce accurate predictions for the system dynamics, while solving a Newtonian equation based on modelling the physical system might be too complex and at times impossible since not all relevant internal system states are known and can be taken into account. In addition with associative learning, the time intervals can be large compared to regular propagation methods. In addition to the general argument given above there are two additional reasons to prefer working with an associated VA interval according to a hemodynamic sensor signal in cardiac pacemakers which are: a superior behaviour of a hemodynamic sensor comparing to the local weak intracardiac electrogram and sensitivity to noise sources that are accumulated in the sensed signals, digitized and processed might be reduced by neural network processing.
CRT is an established therapy for patients with congestive systolic heart failure and intraventricular electrical or mechanical conduction delays, Ellenbogen, Kay and Wilkoff, “Device Therapy for Congestive Heart Failure”, Elsevier Inc. (USA), 2004. CRT is based on synchronized pacing of the two ventricles according to the sensed natural atrium signal that determines the heart rhythm. The resynchronization task demands exact timing of the heart chambers so that the overall stroke volume is maximized for any given heart rate (HR). Optimal timing of activation of the two ventricles is one of the key factors in determining cardiac output. The two major timing parameters which are programmable in a CRT device and determine the pacing intervals are the atrioventricular (AV) delay and interventricular (VV) interval.
Zachary I. Whinnett et al in “Hemodynamic effects of changes in AV and VV delay in cardiac Resynchronization Therapy show a consistent pattern: analysis of shape, magnitude and relative importance of AV and VV delay”, Heart published online, 18 May 2006, doi:10.1136/hrt.2005.080721, studied importance of the AV delay and VV intervals optimization in CHF patients. The authors concluded that changing the AV and VV delay result in a curvilinear and reproducible acute blood pressure response. This shape fits very closely to a parabola, which may be helpful in designing a streamline clinical protocol to select optimal AV and VV delay.
In the present invention the adaptive CRT device control system to an intelligent control system that learns to associate therapeutic actions with input temporal patterns e.g. patterns of stroke volumes that are used for internal representation of heart conditions is further developed. Temporal patterns of stroke volumes are used to improve a reinforcement learning scheme, to classify heart conditions and to associate with the reinforced learning scheme and/or each particular heart condition the learned optimal system therapeutic actions.